/*
 * Javlov - a Java toolkit for reinforcement learning with multi-agent support.
 * 
 * Copyright (c) 2009 Matthijs Snel
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package net.javlov.world.grid;

import net.javlov.Action;
import net.javlov.Agent;
import net.javlov.Function;
import net.javlov.RewardFunction;
import net.javlov.State;
import net.javlov.world.Body;
import net.javlov.world.CollisionEvent;
import net.javlov.world.CollisionListener;

/**
 * Standard impl of a reward function in a gridworld. Assumes reward of -1 on every step,
 * plus any reward configured when colliding with a body such as an obstacle or the goal.
 * 
 * @author Matthijs Snel
 *
 */
public class OldGridRewardFunction implements CollisionListener, RewardFunction {

	protected GridWorld world;
	
	protected GridWorld.RewardBroker broker;
	
	protected double reward, lastPhi = Double.NaN;
	
	protected Function shapingFunction;
	

	public OldGridRewardFunction(GridWorld world) {
		this.world = world;
		shapingFunction = new ZeroFunction();
	}
	
	@Override
	public void collisionOccurred(CollisionEvent e) {
		//TODO assuming body1 belongs to agent that is currently executing action and will
		//thus receive reward; is this valid assumption?
		reward += e.getBody2().getReward();
	}

	@Override
	public double calculateReward(State sprime) {
		double prevLastPhi = lastPhi;
		lastPhi = shapingFunction.eval((double[])sprime.getData())[0];
		double r = reward + lastPhi - prevLastPhi;
		if ( sprime.isTerminal() )
			lastPhi = Double.NaN;
		return r;
	}

	@Override
	public void preAction(Action a, Agent agent, State s) {
		if ( Double.isNaN(lastPhi) ) //only eval first time it's called
			lastPhi = shapingFunction.eval((double[])s.getData())[0];
		reward = -1;		
	}
	
	public Function getShapingFunction() {
		return shapingFunction;
	}

	public void setShapingFunction(Function shapingFunction) {
		this.shapingFunction = shapingFunction;
	}
	
	private static class ZeroFunction implements Function {

		static double[] ret = new double[1];
		
		@Override
		public double[] eval(double[] x) {
			return ret;
		}
		
	}
}
